The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Cross-Validatory Choice of the Number of Components From a Principal Component Analysis
Abstract
A method is described for choosing the number of components to retain in a principal component analysis when the aim is dimensionality reduction. The correspondence between principal component analysis and the singular value decomposition of the data matrix is used. The method is based on successively predicting each element in the data matrix after deleting the corresponding row and column of the matrix, and makes use of recently published algorithms for updating a singular value decomposition. These are very fast, which renders the proposed technique a practicable one for routine data analysis.
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Citations
Cites
Multivariate Statistical Methods
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Singular value decomposition and least squares solutions
Golub, G. H., & Reinsch, C. (1970) · Numerische Mathematik · doi:10.1007/bf02163027 · Cited by 2,929
Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models
Wold, S. (1978) · Technometrics · doi:10.1080/00401706.1978.10489693 · Cited by 2,601
Determining the number of components from the matrix of partial correlations
Velicer, W. F. (1976) · Psychometrika · doi:10.1007/bf02293557 · Cited by 2,438
Multivariate Statistical Methods.
Clarke, G. M., & Morrison, D. F. (1977) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344930 · Cited by 2,165
Pattern recognition by means of disjoint principal components models
Wold, S. (1976) · Pattern Recognition · doi:10.1016/0031-3203(76)90014-5 · Cited by 1,164
Characterization of Some Liquid Phases
McReynolds, W. O. (1970) · Journal of Chromatographic Science · doi:10.1093/chromsci/8.12.685 · Cited by 748
Cross-Validatory Choice and Assessment of Statistical Predictions (With Discussion)
Stone, M. (1976) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1976.tb01573.x · Cited by 686
Multivariate statistical analysis for biologists
Seal, H. L. (1964) · Medical Entomology and Zoology · Cited by 515
Rank-one modification of the symmetric eigenproblem
Bunch, J. R., Nielsen, C. P., & Sorensen, D. C. (1978) · Numerische Mathematik · doi:10.1007/bf01396012 · Cited by 482
Cited by
Wold, S., Esbensen, K., & Geladi, P. (1987) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(87)80084-9 · Cited by 11,606
Abdi, H., & Williams, L. J. (2010) · WIREs Computational Statistics · doi:10.1002/wics.101 · Cited by 11,104
Fast and Efficient Estimation of Individual Ancestry Coefficients
Frichot, E., Mathieu, F., Trouillon, T., Bouchard, G., & François, O. (2014) · Genetics · doi:10.1534/genetics.113.160572 · Cited by 931
Valle, S., Li, W., & Qin, S. J. (1999) · Industrial & Engineering Chemistry Research · doi:10.1021/ie990110i · Cited by 549
Comparing the predictive accuracy of models using a simple randomization test
van der Voet, H. (1994) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(94)85050-x · Cited by 522
Metadata
- Authors
- Eastment, H. T., & Krzanowski, W. J.
- Venue
- Technometrics
- Publisher
- Informa UK Limited
- Published
- 1982-02
- Volume
- 24
- Issue
- 1
- Pages
- 73-77
- ISSN
- 0040-1706 (ISSN-L)
- 1537-2723
- OpenAlex
- W2056961153
- Cited by
- 379
- References
- 16
- FWCI
- 4.74
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
